{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":6960620,"sourceType":"datasetVersion","datasetId":3920517}],"dockerImageVersionId":30579,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport time\nimport matplotlib.pyplot as plt\nimport seaborn as sns","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-11-28T07:48:17.131881Z","iopub.execute_input":"2023-11-28T07:48:17.132910Z","iopub.status.idle":"2023-11-28T07:48:17.137734Z","shell.execute_reply.started":"2023-11-28T07:48:17.132876Z","shell.execute_reply":"2023-11-28T07:48:17.136649Z"},"trusted":true},"execution_count":22,"outputs":[]},{"cell_type":"code","source":"from sklearn.feature_selection import SelectKBest, chi2\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Conv1D, MaxPooling1D, Flatten, LSTM, GRU\nfrom tensorflow.keras.optimizers import Adam\nfrom sklearn.metrics import classification_report\nfrom sklearn.metrics import confusion_matrix\nfrom tensorflow.keras.utils import plot_model","metadata":{"execution":{"iopub.status.busy":"2023-11-28T07:48:17.140035Z","iopub.execute_input":"2023-11-28T07:48:17.140564Z","iopub.status.idle":"2023-11-28T07:48:17.147895Z","shell.execute_reply.started":"2023-11-28T07:48:17.140536Z","shell.execute_reply":"2023-11-28T07:48:17.146968Z"},"trusted":true},"execution_count":23,"outputs":[]},{"cell_type":"code","source":"df = pd.read_csv('/kaggle/input/tayyor-edge-iiotset/Edge-IIoTset_99.csv', low_memory = False)","metadata":{"execution":{"iopub.status.busy":"2023-11-28T07:48:17.149261Z","iopub.execute_input":"2023-11-28T07:48:17.149595Z","iopub.status.idle":"2023-11-28T07:48:54.883519Z","shell.execute_reply.started":"2023-11-28T07:48:17.149570Z","shell.execute_reply":"2023-11-28T07:48:54.882470Z"},"trusted":true},"execution_count":24,"outputs":[]},{"cell_type":"code","source":"df.info()","metadata":{"execution":{"iopub.status.busy":"2023-11-28T07:48:54.884777Z","iopub.execute_input":"2023-11-28T07:48:54.885083Z","iopub.status.idle":"2023-11-28T07:48:54.901384Z","shell.execute_reply.started":"2023-11-28T07:48:54.885058Z","shell.execute_reply":"2023-11-28T07:48:54.900466Z"},"trusted":true},"execution_count":25,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 1945449 entries, 0 to 1945448\nData columns (total 99 columns):\n #   Column                     Dtype  \n---  ------                     -----  \n 0   arp.opcode                 float64\n 1   arp.hw.size                float64\n 2   icmp.checksum              float64\n 3   icmp.seq_le                float64\n 4   icmp.transmit_timestamp    float64\n 5   http.content_length        float64\n 6   http.response              float64\n 7   tcp.ack                    float64\n 8   tcp.ack_raw                float64\n 9   tcp.checksum               float64\n 10  tcp.connection.fin         float64\n 11  tcp.connection.rst         float64\n 12  tcp.connection.syn         float64\n 13  tcp.connection.synack      float64\n 14  tcp.dstport                float64\n 15  tcp.flags                  float64\n 16  tcp.flags.ack              float64\n 17  tcp.len                    float64\n 18  tcp.seq                    float64\n 19  udp.port                   float64\n 20  udp.stream                 float64\n 21  udp.time_delta             float64\n 22  dns.qry.name               float64\n 23  dns.qry.qu                 float64\n 24  dns.retransmission         float64\n 25  dns.retransmit_request     float64\n 26  dns.retransmit_request_in  float64\n 27  mqtt.conflag.cleansess     float64\n 28  mqtt.conflags              float64\n 29  mqtt.hdrflags              float64\n 30  mqtt.len                   float64\n 31  mqtt.msgtype               float64\n 32  mqtt.proto_len             float64\n 33  mqtt.topic_len             float64\n 34  mbtcp.len                  float64\n 35  mbtcp.trans_id             float64\n 36  mbtcp.unit_id              float64\n 37  Attack_label               int64  \n 38  Attack_type                object \n 39  http1_encoded              int64  \n 40  http2_encoded              int64  \n 41  http3_encoded              int64  \n 42  dns_encoded                int64  \n 43  mqtt1_encoded              int64  \n 44  mqtt2_encoded              int64  \n 45  mqtt3_encoded              int64  \n 46  http1_0                    float64\n 47  http1_1                    float64\n 48  http1_2                    float64\n 49  http1_3                    float64\n 50  http1_4                    float64\n 51  http1_5                    float64\n 52  http1_6                    float64\n 53  http1_7                    float64\n 54  http1_8                    float64\n 55  http2_0                    float64\n 56  http2_1                    float64\n 57  http2_2                    float64\n 58  http2_3                    float64\n 59  http2_4                    float64\n 60  http3_0                    float64\n 61  http3_1                    float64\n 62  http3_2                    float64\n 63  http3_3                    float64\n 64  http3_4                    float64\n 65  http3_5                    float64\n 66  http3_6                    float64\n 67  http3_7                    float64\n 68  http3_8                    float64\n 69  http3_9                    float64\n 70  http3_10                   float64\n 71  http3_11                   float64\n 72  http3_12                   float64\n 73  dns_0                      float64\n 74  dns_1                      float64\n 75  dns_2                      float64\n 76  dns_3                      float64\n 77  dns_4                      float64\n 78  dns_5                      float64\n 79  dns_6                      float64\n 80  dns_7                      float64\n 81  dns_8                      float64\n 82  dns_9                      float64\n 83  mqtt1_0                    float64\n 84  mqtt1_1                    float64\n 85  mqtt1_2                    float64\n 86  mqtt1_3                    float64\n 87  mqtt1_4                    float64\n 88  mqtt1_5                    float64\n 89  mqtt1_6                    float64\n 90  mqtt1_7                    float64\n 91  mqtt1_8                    float64\n 92  mqtt1_9                    float64\n 93  mqtt1_10                   float64\n 94  mqtt1_11                   float64\n 95  mqtt1_12                   float64\n 96  mqtt2_0                    float64\n 97  mqtt3_0                    float64\n 98  mqtt3_2                    float64\ndtypes: float64(90), int64(8), object(1)\nmemory usage: 1.4+ GB\n","output_type":"stream"}]},{"cell_type":"code","source":"print(df['Attack_type'].value_counts())","metadata":{"execution":{"iopub.status.busy":"2023-11-28T07:48:54.904393Z","iopub.execute_input":"2023-11-28T07:48:54.904703Z","iopub.status.idle":"2023-11-28T07:48:55.126954Z","shell.execute_reply.started":"2023-11-28T07:48:54.904671Z","shell.execute_reply":"2023-11-28T07:48:55.125831Z"},"trusted":true},"execution_count":26,"outputs":[{"name":"stdout","text":"Attack_type\nNormal                   1399624\nDDoS_UDP                  121567\nDDoS_ICMP                  67939\nSQL_injection              50826\nDDoS_TCP                   50062\nVulnerability_scanner      50026\nPassword                   49933\nDDoS_HTTP                  48544\nUploading                  36957\nBackdoor                   24026\nPort_Scanning              19977\nXSS                        15068\nRansomware                  9689\nFingerprinting               853\nMITM                         358\nName: count, dtype: int64\n","output_type":"stream"}]},{"cell_type":"code","source":"# Creating a dictionary of Types\nattacks = {'Normal': 0,'MITM': 1, 'Uploading': 2, 'Ransomware': 3, 'SQL_injection': 4,\n       'DDoS_HTTP': 5, 'DDoS_TCP': 6, 'Password': 7, 'Port_Scanning': 8,\n       'Vulnerability_scanner': 9, 'Backdoor': 10, 'XSS': 11, 'Fingerprinting': 12,\n       'DDoS_UDP': 13, 'DDoS_ICMP': 14}\ndf['Attack_type'] = df['Attack_type'].map(attacks)","metadata":{"execution":{"iopub.status.busy":"2023-11-28T07:48:55.128302Z","iopub.execute_input":"2023-11-28T07:48:55.128641Z","iopub.status.idle":"2023-11-28T07:48:55.330235Z","shell.execute_reply.started":"2023-11-28T07:48:55.128614Z","shell.execute_reply":"2023-11-28T07:48:55.329379Z"},"trusted":true},"execution_count":27,"outputs":[]},{"cell_type":"code","source":"X = df.drop(columns=['Attack_label', 'Attack_type'])\ny = df['Attack_label']","metadata":{"execution":{"iopub.status.busy":"2023-11-28T07:48:55.331488Z","iopub.execute_input":"2023-11-28T07:48:55.331811Z","iopub.status.idle":"2023-11-28T07:48:55.875076Z","shell.execute_reply.started":"2023-11-28T07:48:55.331784Z","shell.execute_reply":"2023-11-28T07:48:55.873973Z"},"trusted":true},"execution_count":28,"outputs":[]},{"cell_type":"code","source":"# Apply the Chi-Squared test\nchi_selector = SelectKBest(chi2, k='all')  # Set k to the desired number of features\nX_kbest = chi_selector.fit_transform(X, y)","metadata":{"execution":{"iopub.status.busy":"2023-11-28T07:48:55.876282Z","iopub.execute_input":"2023-11-28T07:48:55.876583Z","iopub.status.idle":"2023-11-28T07:48:58.770538Z","shell.execute_reply.started":"2023-11-28T07:48:55.876557Z","shell.execute_reply":"2023-11-28T07:48:58.769495Z"},"trusted":true},"execution_count":29,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:767: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:767: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n","output_type":"stream"}]},{"cell_type":"code","source":"# Get the scores for each feature\nchi_scores = chi_selector.scores_\n\n# Combine scores with feature names\nchi_scores = pd.DataFrame({'feature': X.columns, 'score': chi_scores})\n\n# Sort the features by their scores\nchi_scores = chi_scores.sort_values(by='score', ascending=False)\n\nprint(chi_scores)","metadata":{"execution":{"iopub.status.busy":"2023-11-28T07:48:58.771759Z","iopub.execute_input":"2023-11-28T07:48:58.772104Z","iopub.status.idle":"2023-11-28T07:48:58.782467Z","shell.execute_reply.started":"2023-11-28T07:48:58.772076Z","shell.execute_reply":"2023-11-28T07:48:58.781469Z"},"trusted":true},"execution_count":30,"outputs":[{"name":"stdout","text":"         feature         score\n8    tcp.ack_raw  1.460557e+14\n7        tcp.ack  7.606459e+13\n18       tcp.seq  7.310061e+12\n20    udp.stream  4.093708e+11\n22  dns.qry.name  4.196200e+10\n..           ...           ...\n89       mqtt1_8  1.169939e+00\n84       mqtt1_3  7.799595e-01\n90       mqtt1_9  7.799595e-01\n91      mqtt1_10  7.799595e-01\n85       mqtt1_4  7.799595e-01\n\n[97 rows x 2 columns]\n","output_type":"stream"}]},{"cell_type":"code","source":"selected_features = chi_scores['feature'].tolist()[:93]  # Select top k features","metadata":{"execution":{"iopub.status.busy":"2023-11-28T07:48:58.783852Z","iopub.execute_input":"2023-11-28T07:48:58.784138Z","iopub.status.idle":"2023-11-28T07:48:58.796267Z","shell.execute_reply.started":"2023-11-28T07:48:58.784114Z","shell.execute_reply":"2023-11-28T07:48:58.795366Z"},"trusted":true},"execution_count":31,"outputs":[]},{"cell_type":"code","source":"# Split the data into train (80%) and test (20%) sets\nX_train, X_test, y_train, y_test = train_test_split(X[selected_features], y, test_size=0.2, random_state=42)\n\n# Split the training data further into train (80%) and validation (20%) sets\nX_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.125, random_state=42)","metadata":{"execution":{"iopub.status.busy":"2023-11-28T07:48:58.797384Z","iopub.execute_input":"2023-11-28T07:48:58.797661Z","iopub.status.idle":"2023-11-28T07:49:02.595550Z","shell.execute_reply.started":"2023-11-28T07:48:58.797629Z","shell.execute_reply":"2023-11-28T07:49:02.594507Z"},"trusted":true},"execution_count":32,"outputs":[]},{"cell_type":"code","source":"print(\"X_train shape:\", X_train.shape)\nprint(\"X_val shape:\", X_val.shape)\nprint(\"X_test shape:\", X_test.shape)","metadata":{"execution":{"iopub.status.busy":"2023-11-28T07:49:02.596821Z","iopub.execute_input":"2023-11-28T07:49:02.597169Z","iopub.status.idle":"2023-11-28T07:49:02.602980Z","shell.execute_reply.started":"2023-11-28T07:49:02.597140Z","shell.execute_reply":"2023-11-28T07:49:02.602047Z"},"trusted":true},"execution_count":33,"outputs":[{"name":"stdout","text":"X_train shape: (1361814, 93)\nX_val shape: (194545, 93)\nX_test shape: (389090, 93)\n","output_type":"stream"}]},{"cell_type":"code","source":"scaler = StandardScaler().fit(X_train)\nX_train = scaler.transform(X_train)\nX_val = scaler.transform(X_val)\nX_test = scaler.transform(X_test)","metadata":{"execution":{"iopub.status.busy":"2023-11-28T07:49:02.604292Z","iopub.execute_input":"2023-11-28T07:49:02.604637Z","iopub.status.idle":"2023-11-28T07:49:05.310503Z","shell.execute_reply.started":"2023-11-28T07:49:02.604605Z","shell.execute_reply":"2023-11-28T07:49:05.309475Z"},"trusted":true},"execution_count":34,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:767: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:767: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:767: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:767: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n","output_type":"stream"}]},{"cell_type":"code","source":"def cnn_lstm_gru_model(input_shape, num_classes):\n    \n    model = Sequential([\n        Conv1D(filters=32, kernel_size=3, activation='relu', input_shape=input_shape),        \n        MaxPooling1D(pool_size=2),\n        \n        Conv1D(filters=64, kernel_size=3, activation='relu'),\n        MaxPooling1D(pool_size=2),\n        \n        LSTM(64, return_sequences=True),\n        GRU(64, return_sequences=False),\n        \n        Flatten(),\n        \n        Dense(128, activation='relu'),\n        Dropout(0.5),\n        Dense(num_classes, activation='sigmoid')\n    ])\n\n    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n    return model\n\ninput_shape = (X_train.shape[1], 1)\nnum_classes = 1\nmodel = cnn_lstm_gru_model(input_shape, num_classes)\nmodel.summary()\nplot_model(model)","metadata":{"execution":{"iopub.status.busy":"2023-11-28T07:49:05.315082Z","iopub.execute_input":"2023-11-28T07:49:05.315493Z","iopub.status.idle":"2023-11-28T07:49:06.010971Z","shell.execute_reply.started":"2023-11-28T07:49:05.315467Z","shell.execute_reply":"2023-11-28T07:49:06.009598Z"},"trusted":true},"execution_count":35,"outputs":[{"name":"stdout","text":"Model: \"sequential_1\"\n_________________________________________________________________\n Layer (type)                Output Shape              Param #   \n=================================================================\n conv1d_2 (Conv1D)           (None, 91, 32)            128       \n                                                                 \n max_pooling1d_2 (MaxPoolin  (None, 45, 32)            0         \n g1D)                                                            \n                                                                 \n conv1d_3 (Conv1D)           (None, 43, 64)            6208      \n                                                                 \n max_pooling1d_3 (MaxPoolin  (None, 21, 64)            0         \n g1D)                                                            \n                                                                 \n lstm_1 (LSTM)               (None, 21, 64)            33024     \n                                                                 \n gru_1 (GRU)                 (None, 64)                24960     \n                                                                 \n flatten_1 (Flatten)         (None, 64)                0         \n                                                                 \n dense_2 (Dense)             (None, 128)               8320      \n                                                                 \n dropout_1 (Dropout)         (None, 128)               0         \n                                                                 \n dense_3 (Dense)             (None, 1)                 129       \n                                                                 \n=================================================================\nTotal params: 72769 (284.25 KB)\nTrainable params: 72769 (284.25 KB)\nNon-trainable params: 0 (0.00 Byte)\n_________________________________________________________________\n","output_type":"stream"},{"execution_count":35,"output_type":"execute_result","data":{"image/png":"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","text/plain":"<IPython.core.display.Image object>"},"metadata":{}}]},{"cell_type":"code","source":"train_start_time = time.time()\n# Train the model\nhistory = model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=6, batch_size=32)\n# Record the ending time\ntrain_end_time = time.time()\n\n# Record the starting time for testing\ntest_start_time = time.time()\n# Evaluate the model\nloss, accuracy = model.evaluate(X_test, y_test, batch_size=32)\n# Record the ending time for testing\ntest_end_time = time.time()\n\nprint(f'Test Loss: {loss:.4f}')\nprint(f'Test Accuracy: {accuracy:.4f}')\n\n# Calculate and print the training time\ntrain_time = train_end_time - train_start_time\nprint(f\"Training time: {train_time:.2f} seconds\")\n\n# Calculate and print the testing time\ntest_time = test_end_time - test_start_time\nprint(f\"Testing time: {test_time:.2f} seconds\")","metadata":{"execution":{"iopub.status.busy":"2023-11-28T07:49:06.013043Z","iopub.execute_input":"2023-11-28T07:49:06.013801Z","iopub.status.idle":"2023-11-28T08:23:15.000539Z","shell.execute_reply.started":"2023-11-28T07:49:06.013760Z","shell.execute_reply":"2023-11-28T08:23:14.999621Z"},"trusted":true},"execution_count":36,"outputs":[{"name":"stdout","text":"Epoch 1/6\n42557/42557 [==============================] - 351s 8ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 6.0838e-07 - val_accuracy: 1.0000\nEpoch 2/6\n42557/42557 [==============================] - 341s 8ms/step - loss: 1.3060e-04 - accuracy: 1.0000 - val_loss: 4.6583e-11 - val_accuracy: 1.0000\nEpoch 3/6\n42557/42557 [==============================] - 340s 8ms/step - loss: 8.1947e-05 - accuracy: 1.0000 - val_loss: 1.1399e-15 - val_accuracy: 1.0000\nEpoch 4/6\n42557/42557 [==============================] - 338s 8ms/step - loss: 2.1156e-10 - accuracy: 1.0000 - val_loss: 4.3320e-19 - val_accuracy: 1.0000\nEpoch 5/6\n42557/42557 [==============================] - 317s 7ms/step - loss: 1.4664e-11 - accuracy: 1.0000 - val_loss: 7.3978e-20 - val_accuracy: 1.0000\nEpoch 6/6\n42557/42557 [==============================] - 317s 7ms/step - loss: 7.5520e-11 - accuracy: 1.0000 - val_loss: 7.3541e-21 - val_accuracy: 1.0000\n12160/12160 [==============================] - 43s 4ms/step - loss: 1.7052e-20 - accuracy: 1.0000\nTest Loss: 0.0000\nTest Accuracy: 1.0000\nTraining time: 2005.59 seconds\nTesting time: 43.38 seconds\n","output_type":"stream"}]},{"cell_type":"code","source":"# Plot the training and validation accuracy over the epochs\nplt.plot(history.history['accuracy'])\nplt.plot(history.history['val_accuracy'])\nplt.title('Model accuracy')\nplt.ylabel('Accuracy')\nplt.xlabel('Epoch')\nplt.legend(['Train', 'Test'], loc='upper left')\nplt.show()\nplt.savefig('acc_plo.jpg')","metadata":{"execution":{"iopub.status.busy":"2023-11-28T08:23:15.001722Z","iopub.execute_input":"2023-11-28T08:23:15.002005Z","iopub.status.idle":"2023-11-28T08:23:15.307316Z","shell.execute_reply.started":"2023-11-28T08:23:15.001980Z","shell.execute_reply":"2023-11-28T08:23:15.306377Z"},"trusted":true},"execution_count":37,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 0 Axes>"},"metadata":{}}]},{"cell_type":"code","source":"# Plot the training and validation loss over the epochs\nplt.plot(history.history['loss'])\nplt.plot(history.history['val_loss'])\nplt.title('Model loss')\nplt.ylabel('Loss')\nplt.xlabel('Epoch')\nplt.legend(['Train', 'Test'], loc='upper left')\nplt.show()\nplt.savefig('los_plo.jpg')","metadata":{"execution":{"iopub.status.busy":"2023-11-28T08:23:15.308542Z","iopub.execute_input":"2023-11-28T08:23:15.308847Z","iopub.status.idle":"2023-11-28T08:23:15.531850Z","shell.execute_reply.started":"2023-11-28T08:23:15.308821Z","shell.execute_reply":"2023-11-28T08:23:15.530930Z"},"trusted":true},"execution_count":38,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 0 Axes>"},"metadata":{}}]},{"cell_type":"code","source":"y_pred = model.predict(np.expand_dims(X_test, axis=2))","metadata":{"execution":{"iopub.status.busy":"2023-11-28T08:23:15.533049Z","iopub.execute_input":"2023-11-28T08:23:15.533681Z","iopub.status.idle":"2023-11-28T08:23:55.318360Z","shell.execute_reply.started":"2023-11-28T08:23:15.533647Z","shell.execute_reply":"2023-11-28T08:23:55.317512Z"},"trusted":true},"execution_count":39,"outputs":[{"name":"stdout","text":"12160/12160 [==============================] - 35s 3ms/step\n","output_type":"stream"}]},{"cell_type":"code","source":"# Performance comparison table\nprint(classification_report(y_test, np.round(y_pred), target_names=['No Intrusion', 'Intrusion']))","metadata":{"execution":{"iopub.status.busy":"2023-11-28T08:23:55.319705Z","iopub.execute_input":"2023-11-28T08:23:55.320061Z","iopub.status.idle":"2023-11-28T08:23:56.296809Z","shell.execute_reply.started":"2023-11-28T08:23:55.320025Z","shell.execute_reply":"2023-11-28T08:23:56.295831Z"},"trusted":true},"execution_count":40,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n","output_type":"stream"},{"name":"stdout","text":"              precision    recall  f1-score   support\n\nNo Intrusion       1.00      1.00      1.00    279968\n   Intrusion       1.00      1.00      1.00    109122\n\n    accuracy                           1.00    389090\n   macro avg       1.00      1.00      1.00    389090\nweighted avg       1.00      1.00      1.00    389090\n\n","output_type":"stream"}]},{"cell_type":"code","source":"# Compute the confusion matrix\nconf_mat = confusion_matrix(y_test, np.round(y_pred))\n\n# Define the class labels\nclass_labels = ['No Intrusion', 'Intrusion']\n\n# Create a heatmap plot of the confusion matrix\nsns.heatmap(conf_mat, annot=True, fmt='d', cmap='Blues', xticklabels=class_labels, yticklabels=class_labels)\n\n# Set the plot labels and title\nplt.xlabel('Predicted Label')\nplt.ylabel('True Label')\nplt.title('Confusion Matrix')\n\n# Show the plot\nplt.show()\nplt.savefig('con_max.jpg')","metadata":{"execution":{"iopub.status.busy":"2023-11-28T08:23:56.298279Z","iopub.execute_input":"2023-11-28T08:23:56.298937Z","iopub.status.idle":"2023-11-28T08:23:56.678329Z","shell.execute_reply.started":"2023-11-28T08:23:56.298897Z","shell.execute_reply":"2023-11-28T08:23:56.677400Z"},"trusted":true},"execution_count":41,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 2 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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 0 Axes>"},"metadata":{}}]},{"cell_type":"code","source":"# Predict the test set\ny_pred = (y_pred > 0.5)\n\n# Compute confusion matrix\ncm = confusion_matrix(y_test, y_pred)\ncm_norm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n# Define the class labels\nclass_labels = ['No Intrusion', 'Intrusion']\n# Print confusion matrix as heatmap with percentages\nplt.figure(figsize=(8, 6))\nsns.heatmap(cm_norm, annot=True, cmap='Blues', xticklabels=class_labels, yticklabels=class_labels, fmt='.2%')\nplt.xlabel('Predicted labels')\nplt.ylabel('True labels')\nplt.title('Normalized Confusion Matrix as Percentages')\nplt.show()\nplt.savefig('con_per.jpg')","metadata":{"execution":{"iopub.status.busy":"2023-11-28T08:23:56.679474Z","iopub.execute_input":"2023-11-28T08:23:56.679765Z","iopub.status.idle":"2023-11-28T08:23:56.964390Z","shell.execute_reply.started":"2023-11-28T08:23:56.679739Z","shell.execute_reply":"2023-11-28T08:23:56.963428Z"},"trusted":true},"execution_count":42,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x600 with 2 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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 0 Axes>"},"metadata":{}}]}]}